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data scientists, software engineers, biomedical researchers, and clinicians. Your research will focus on developing AI- and LLM-enabled methods and tools to structure, harmonise, and analyse clinical
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: Engineering design & CAE (e.g., structural/thermal/fluids workflows) Chemical and process industries (e.g. optimization, control, surrogate models) Related computational engineering problems where simulation
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, or molecular genetics. Experience with 3D genomics, single-cell genomics, and advanced imaging approaches (confocal and super-resolution microscopy). Expertise in proteomics, protein structure–function
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novel solutions use parts of these models for planning or control, but they do not take full advantage of the structured, layered information such graphs so far. Therefore, our project aims to tightly
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: structured environments, such as the synthetic cells; environments with competing interaction partners; and systems with hybrid RNA-DNA duplexes. You should have a degree in a subject related to physical
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publications in bioinformatics analysis of large-scale biomedical data, e.g., omics, clinical, structural bioinformatics, other biomedical data, should be outlined in the CV Demonstrated skills and knowledge in
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data handling skills Understanding of data structures, data modelling and data management Ability to write robust, reproducible code in Python or R Experience writing academic publications independently
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Study static spin structures and compute the dynamic (time-dependent) magnetization response, where the intermediate scattering function serves as the key neutron scattering observable Contribute
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to rapidly learn Desirable Proficiency in Yiddish Knowledge of information theory is desirable, as is additional mathematical background Background using phrase-structure-parsed corpora (e.g. Penn Treebank
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fieldwork, multi-taxa datasets, or trait-based ecology is considered an advantage. What we offer A full-time doctoral researcher position for three years A structured and supportive PhD training environment